Dr. Theodore J. Noseworthy is an Associate Dean (External Relations) and Professor of Marketing at the Schulich School of Business, York University, and holds a Tier II Canada Research Chair in Entrepreneurial Innovation and the Public Good. He leads the NOESIS: Innovation, Design, and Consumption Laboratory, focusing on consumer behavior, product innovation, and marketing technology. His work explores how consumers perceive and interact with new products, including innovations in food, technology, and finance. His research interests span consumer information processing, the psychological impact of automation in food services, social media engagement dynamics, and the societal implications of innovations like genetically modified foods. He has been recognized for contributions to student accessibility, research leadership, and impactful scholarship, including awards from York University, the Journal of Consumer Research, and the Ontario Early Researcher Award. Dr. Noseworthy’s grants include projects on predicting crowdfunding success via visual analytics, consumer behavior during the pandemic, and the ethical communication of innovations. His lab fosters interdisciplinary collaboration to bridge academic insights with real-world applications in marketing and policy.
Dr. Alina Lazar is a Professor in the Department of Computer Science & Information Systems at Youngstown State University (YSU), part of the STEM College. She holds a Ph.D. in Computer Science from Wayne State University (2002) and a BS from Western University of Timisoara, Romania (1995). Her research focuses on Machine Learning, Data Science, and High Energy Physics applications, including Graph Neural Networks for particle tracking and traffic modeling using probe vehicle data. She has received multiple awards, including YSU Distinguish Professorships for Teaching (2025), Research (2022), and Service (2017). Lazar has secured a $600k grant from the DOE’s FAIR initiative for her work on large-scale models in HEP pattern recognition. Her teaching includes advanced courses like Deep Learning and Data Science. She has advised numerous master's theses and projects, with students focusing on topics like bug detection, database systems, and code readability. Lazar serves on NSF and DOE review panels and chairs conferences like OCWiC. Her interdisciplinary collaborations span physics (Lawrence Berkeley Lab), transportation, and software engineering.
Stuart M. Shieber is the James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science at Harvard University's School of Engineering and Applied Sciences (SEAS), with affiliations in Linguistics and Philosophy. He specializes in computational linguistics, exploring intersections of computer science, linguistics, and artificial intelligence. His research spans natural language processing, formal grammar, machine translation, and interdisciplinary areas like automated graphic design, privacy mechanisms, and computational biology. Education: AB summa cum laude in Applied Mathematics (Harvard College, 1981); PhD in Computer Science (Stanford University, 1989) Research Interests: Synchronous grammars, psycholinguistic modeling, privacy-preserving auctions, and open-access publishing policies. Awards: Presidential Young Investigator Award (1991), multiple fellowships, and honorary chairs like Harvard College Professorship (2001). Courses: CS51 (Abstraction and Design), CS187 (Computational Linguistics), and Empirical and Mathematical Reasoning courses. Grants & Roles: Founder of the Center for Research on Computation and Society, director of Harvard's Office for Scholarly Communication, and co-founder of Cartesian Products Inc. and Microtome Publishing. His work on synchronous tree-adjoining grammars and the philosophical underpinnings of the Turing Test has been seminal. Recent publications address neural language models, causal syntax analysis, and conversational AI.
Sean Trott is Assistant Teaching Professor in Cognitive Science at UC San Diego, specializing in language comprehension, computational modeling, and large language models. His research examines how humans and language models represent meaning and resolve ambiguity. Research combines behavioral experiments, corpus analysis, and LLM probing to investigate lexical ambiguity, context effects, and semantic representation. Current projects explore cognitive plausibility of LLMs and computational accounts of language processing. Publications analyze word meaning representation, theory of mind capabilities in LLMs, cross-linguistic differences in language processing, and educational applications of large language models. Recent work appears in Psychological Review, Cognitive Science, and ACL proceedings. Teaching covers computational social science, programming, statistics, and language-related courses. Developed the RAW-C dataset for studying ambiguous words in context. Maintains the Counterfactual newsletter on cognitive science and AI. Laboratory develops methods for using LLMs as cognitive models and evaluates their alignment with human language processing.
Rolf Schwitter is a Senior Lecturer at the School of Computing, Macquarie University. His research focuses on natural and formal language processing, including controlled natural languages, answer extraction, knowledge representation, probabilistic logic programming, and Semantic Web technologies. He holds an h-index of 13 with over 944 citations. His work emphasizes bridging human-readable and machine-processable systems, particularly in legal and technical domains. Key research contributions include developing frameworks like PENG ASP for declarative programming in natural language, smart contract systems, and hybrid explainability tools like HESIP. He leads projects such as Policy Automation: Reconstructing Policy Documents for Objective Decision Making (2018–present) and has collaborated on initiatives like TwitterNews+ for real-time event detection. Research Themes: Controlled Natural Languages, Smart Contracts, Explainable AI, Legal Informatics Notable Projects: User-guided legal document processing, error-free smart contracts, hybrid prediction explanations Received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in academic publishing with over 117 research outputs spanning conferences, journals, and book chapters.
Morgan Ryan Frank is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh’s School of Computing and Information. He holds affiliations as an MIT Connection Science Fellow, Research Affiliate at MIT’s Media Lab, Digital Fellow at Stanford’s Institute for Human-Centered AI, and Fellow at Microsoft’s AI Economy Institute. His research focuses on the complexity of AI, future of work, and socio-economic impacts of technological change, leveraging tools from labor economics, network science, and computational social science. Frank earned his PhD from MIT’s Media Lab (2019), focusing on scalable cooperation in technology’s societal impact. His postdoctoral work at MIT explored urban labor markets using data science. Earlier degrees include a Master’s and Bachelor’s from the University of Vermont, with interdisciplinary research in complex systems, happiness dynamics, and climate modeling. His research spans AI’s labor market effects, green job transitions, and urban resilience. Notable contributions include work on AI-driven unemployment risk prediction and spatial constraints on fossil fuel worker retraining. Frank’s interdisciplinary approach addresses systemic challenges through data-driven policy recommendations. Scientific accolades include fellowships from MIT, Stanford, and Microsoft. His work bridges academic research with real-world policy, emphasizing equitable outcomes in technological adoption. Advising and grants are not explicitly detailed in the text, but his extensive publication record reflects collaborative projects with institutions globally. He is affiliated with multiple labs, including Pitt’s Institute for Cyber Law and Stanford’s Digital Economy Lab, fostering interdisciplinary innovation.
Prof. Dr. Patrick Delfmann is a University Professor at the Department of Computer Science (FB4) of the University of Koblenz, leading the Process Science research group. His roles include Research Dean of the Department and chairman of the Institute for Business and Administrative Information Systems. He holds a Dr. rer. pol. from the University of Münster (2006) and has held academic positions since 2002, including senior academic councillor roles and acting professorships before his current position since 2017. His research focuses on technological aspects of business process management, including process mining, predictive process monitoring, and ontology-based process engineering. Current projects include AI-DPA (funded by Rhineland-Palatinate) and DFG-funded MIB (declarative process models). Methodological foundations include algorithmic graph theory, computational linguistics, and quantum machine learning. Key achievements include the 2024 Best Paper Award at ICPM’s PODS4H workshop for process-oriented cancer data analysis. He advises on interdisciplinary theses requiring strong algorithmic and modeling skills, and collaborates with industry partners to ensure practical applicability of research outcomes. Education: PhD in Business Administration (2006), University of Münster; earlier roles as research assistant (2001–2013). Grants: DFG MIB Project (2023–), RLP AI-DPA Research College (2023–). Labs/Teams: Process Science Group develops tools like declare-js and ProPoneRe, focusing on predictive modeling and process compliance.
Bas Kempen is a PE&RC Research Associate at ISRIC - World Soil Information , part of Wageningen University & Research. His work focuses on Digital Soil Mapping , Tropical Soils , and Machine Learning Applications in soil science. He leads projects like Validatie BVU and contributes to SoilGrids , a global soil data infrastructure initiative. PhD candidate in Digital Soil Mapping (2006-2011) Co-promotor for Mathematical Modeling projects Research highlights include: Transfer functions for nutrient modeling in tropical regions Bayesian statistics for soil health data collection Spatial yield predictions using QUEFTS in Sub-Saharan Africa Development of SoilGrids 2.0 with quantified uncertainty His collaborations span Rwanda, Kenya, India, and Malawi. As Project Leader for BVU validation (2010-2014), he established standardized soil data exchange protocols.
Predrag Janičić is a full professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been actively contributing to the fields of artificial intelligence, automated reasoning, and mathematical software development for over two decades. His work bridges theoretical computer science with practical applications in education and research. His educational background includes a BSc (1993, GPA 10 out of 10), MSc (1996), and PhD (2001), all in Computer Science from the Faculty of Mathematics, University of Belgrade. His academic journey began with excellence, having won the First-placed at Yugoslav federal competition in mathematics (1987) and the Best student award of University of Belgrade (1993). Professor Janičić's research spans multiple interconnected domains within computer science and mathematics. His primary focus is on automated reasoning systems, particularly in synthetic geometry, where he has developed innovative approaches that combine constraint solving with coherent logic. He has made significant contributions to the field of theorem proving, creating systems capable of generating both formal and human-readable proofs. His work on geometry constructions has led to practical tools used worldwide for mathematical visualization and education. The integration of artificial intelligence techniques with mathematical reasoning forms another important thread in his research portfolio, demonstrating how computational methods can enhance mathematical discovery and education. His recent publications show a consistent focus on advancing automated reasoning in geometry, with particular emphasis on making theorem proving more accessible through visualization and constraint-based approaches. The trend in his work demonstrates a progression from theoretical foundations to practical implementations, with an increasing focus on making complex mathematical reasoning understandable and usable for broader audiences. His research has evolved to incorporate modern computational techniques while maintaining strong connections to classical mathematical problems. First-placed at Yugoslav federal competition in mathematics (1987) Best student award of University of Belgrade (1993) City of Belgrade Award (2004) Professor Janičić has successfully advised four PhD students to completion, with the most recent graduation in 2016. His research has been supported by numerous prestigious grants including those from British Scholarship Trust (UK), EPSRC (UK), Coimbra Group Hospitality Scheme (Portugal), DAAD (Germany), OAD (Austria), Egide/Pavle Savic (France/Serbia), COST IC0901 (EU), SCOPES (Switzerland), and as grant holder for Serbian Ministry of Science projects 144030 (2006-2010) and 174021 (2011-2019) titled 'Automated Reasoning and Data Mining'. He leads the Automated Reasoning GrOup (ARGO) which has produced significant work in automated theorem proving, geometric reasoning, and SAT/SMT solving. The group's flagship project, GCLC (Geometry Constructions -> LaTeX Converter), has evolved from a simple LaTeX figure generator to a comprehensive mathematical visualization and theorem proving platform with thousands of users worldwide. The group maintains active collaborations with institutions across Europe and has contributed to numerous international conferences and research projects in automated reasoning.
Kin Lo is an Associate Professor in the Accounting and Information Systems Division at the Sauder School of Business, University of British Columbia. He holds a BCom from the University of Calgary, an MS, and a PhD from Northwestern University. His primary research focuses on empirical financial accounting, voluntary disclosures, regulated reporting regimes, and the role of accounting in equity valuation. Education: BCom (University of Calgary) MS (Northwestern University) PhD (Northwestern University) Kin's research explores the intersection of financial accounting, disclosure practices, and market behavior. His recent publications examine topics such as CEO integrity in analyst forecasts, investor influence on analysts' behavior, and the impact of mental health on financial professionals. His publications span a range of subfields including earnings management, analyst behavior, executive compensation, and disclosure strategies. Current courses taught include Taxes and Decision Making, reflecting his expertise in financial accounting applications.
Richard Khoury is a Researcher at Laval University's Computer Vision and Systems Laboratory within the Faculty of Science and Engineering. He holds a Doctorate in Computer Engineering from the University of Waterloo (expected 2007), a Master's in Electrical Engineering from Laval University (2004), and a Baccalaureate in Computer Engineering from the same institution (2002). His research focuses on Natural Language Processing, Machine Learning, and Cyberbullying Detection, with contributions to recommendation systems, toxic comment analysis, and health informatics. He has supervised collaborative projects and published extensively in top-tier venues. Key research areas include semantic analysis, conversational AI, and data mining applications in healthcare and media studies. His recent work spans contextual integrity in polypharmacy analysis, automated journalistic question extraction, and synthetic bilingual contract generation. Collaborations include projects on user frustration prediction in conversational systems and pandemic-era media communication. He actively contributes to the development of NLP tools for French-language processing and ethical considerations in AI applications.
Dr. Asma Patel is a Teaching Fellow at Aston University , affiliated with the College of Business and Social Sciences and the Business Analytics and Information Systems department. Her research spans interdisciplinary domains at the intersection of neuroscience, cybersecurity, and machine learning. PhD in Computing (2018) MSc in Computer Science (2012) BSc in Computer Science (2010) Key research areas include neuroscience-informed AI frameworks , bio-inspired computing models , machine-readable privacy notices , and malware incident management . Her work applies neural networks to neurofinance and forex prediction while addressing cybersecurity challenges in security operations centers. Recent publications highlight collaborations across institutions, with notable contributions to journals like IEEE Transactions on Technology and Society and conferences such as ICISSP 2024. Research trends emphasize artificial intelligence , data privacy , and adversarial neural networks . While no explicit grants or scientific awards are documented in the provided materials, her work has garnered attention in 3 news outlets , 8 X (Twitter) references , and 43 Mendeley readers for specific outputs. She actively supervises PhD students and contributes to the Cyber Security Innovation (CSI) Research Centre .
Jamal Nabhani serves as Assistant Professor of Clinical Urology at the University of Southern California and Assistant Dean for Clinical Administration at Los Angeles General Hospital. His dual roles focus on advancing urological care through academic research and clinical leadership within public health systems serving diverse populations. Dr. Nabhani's research program integrates cutting-edge technology with clinical practice, emphasizing: Artificial intelligence applications for outpatient urology decision support Optimized kidney stone management in public health systems Male infertility mechanisms following testicular trauma Prostate cancer care transitions for low-income populations Robotic and laparoscopic surgical innovations Testicular cancer pathophysiology and metastasis patterns Analysis of his 2021-2025 publications reveals a consistent focus on leveraging AI to improve patient communication and clinical workflows while addressing healthcare disparities in public systems. His work on integrated care models for kidney stone patients and insurance transition challenges for prostate cancer patients demonstrates commitment to vulnerable populations. Surgical technique refinements, particularly in complex tumor thrombectomy and image-guided biopsies, complement his health services research. As Assistant Dean for Clinical Administration at Los Angeles General Hospital, Dr. Nabhani shapes clinical service delivery while maintaining active research productivity. His scholarly contributions appear in leading urology journals including European Urology, Journal of Urology, and Urology, reflecting significant impact in both clinical and translational domains.
Giuliana Spadaro is an Assistant Professor at the Faculty of Behavioural and Movement Sciences (Social Psychology Department) and holds affiliated roles at the IBBA and the Amsterdam Sustainability Institute at Vrije Universiteit Amsterdam. Her research focuses on social psychology topics such as cooperation in social dilemmas, conspiracy theories, and meta-analytic methods. She has received notable awards including the APS Rising Star Award (2024) and the NWO Open Science Fund (2023) , reflecting her contributions to open science and behavioral research. Her work bridges interdisciplinary themes like AI ethics, environmental psychology, and cross-cultural studies. Spadaro’s research interests are driven by UN Sustainable Development Goals related to sustainable communities and responsible consumption. She has contributed to over 25 peer-reviewed publications, including influential studies on artificial intelligence distrust, institutional trust dynamics, and automated hypothesis generation in cooperation research. Her editorial roles include co-editing Journal of Experimental Social Psychology and reviewing for journals like PLoS ONE and Cognition . Key achievements include developing the Cooperation Databank for accelerating research synthesis and advancing machine learning applications in behavioral science. She has supervised one PhD thesis and teaches courses like Advanced Research Methods . Her grants include collaborative projects on open science and institutional trust mechanisms.
Ilaria Tiddi is an Assistant Professor at the Faculty of Science, Department of Computer Science at Vrije Universiteit Amsterdam. She is also affiliated with the Network Institute and the Artificial Intelligence research group. Her work focuses on hybrid intelligence, knowledge graphs, robotic perception, and multi-agent systems. She contributes to the UN Sustainable Development Goals through her research in artificial intelligence and robotics. Her research interests include explainable AI, knowledge representation, robotic knowledge acquisition, and the integration of semantic technologies into robotic systems. She explores how knowledge graphs can enhance robotic perception and decision-making, particularly in dynamic environments. Recent work emphasizes shared understanding in open multi-agent systems and actionable knowledge extraction for robotic tasks. Dr. Tiddi has published extensively on topics like ontology design for robotics, data augmentation in reinforcement learning, and narrative-based societal understanding. Her articles highlight interdisciplinary approaches combining AI with fields like bioinformatics and urban studies. She teaches courses on artificial intelligence and semantic technologies, reflecting her commitment to advancing computational methods for complex problem-solving. Her datasets and software, such as the MUHAI Benchmark and story-generation frameworks, demonstrate her focus on practical applications of AI. Collaborations span international institutions, addressing challenges in robotic perception, ethical AI, and hybrid intelligence systems.
